1. Field of the Invention
The present invention relates generally to the field of verifying the identification of the holder of an identification card, using signature verification methods. More specifically, the motion and pressure characteristics experienced by a pen while the signature is being signed are converted into digital data and compressed for storage in the memory of an identification card. Later, when the identity of a holder of the identification card is to be verified, a pen having substantially similar motion and pressure transducing characteristics is used by the person to generate a set of trial data while signing a trial signature. The trial data is matched to the expanded stored reference data and the correlation and coherence is a measure of the veracity of the identification of the holder.
2. Prior Art
Although a number of identification verification systems are in use today, the most popular is the use of a secret number or personal identification number, theoretically known only to the authorized holder of an identification card. This secret number is related to the identification card number, either by cryptographic algorithms or by data in a data base.
Verifying the identification of a person by other methods, including biological characteristics of a person, have been suggested. Identity by comparison of written signatures has long been practiced manually in executing financial transactions. More recently, precise mechanisms have been developed for capturing the dynamic motion characteristics experienced by a pen while a person attempts to sign a name for verifying his or her identity. The dynamic motion characteristics are then compared in a computer with the dynamic characteristic stored at the time an authorized holder of the card was enrolled into the computer system.
An example of this method is taught in U.S. Pat. Nos. 3,983,535 and 4,128,829 by Herbst et al. Comparison of the trial signature with the reference signature is accomplished by segmenting each block of signature data into similar segments and individually comparing similar segments using correlation and shifting of the segments to find regions of high correlation. Signature segmentation is taught by Chainer et al. in U.S. Pat. No. 4,553,258.
In addition to segmenting, it is known in the art to develop a similarity measure between two signatures. The similarity measure is a weighted function of the correlation, coherence and segmentation of the two signatures. Similarity and how it is derived from correlation, coherence and segmentation is described by S. Gundersen in U.S. Pat. Nos. 4,736,445 and 4,789,934.
An adaptive means of verification is described by Williford in U.S. Pat. No. 4,724,542.
These methods require that large amounts of data be stored for each signature in order to have high fidelity correlation and similarity measures. The requirement for storing large amounts of data has heretofore made storage of reference signature data directly on an identification card impractical. This difficulty is further accentuated when one realizes that signatures are of varying lengths. In a computer data base, reference signatures are stored end to end and therefore space need only be provided for the number of signatures to be stored times the average signature length. When signatures are stored on an identification card, space must be provided on the card for the maximum length signature.
The digital data corresponding to the dynamics of a person's signature is basically random in nature and therefore existing data compression techniques such as used in compressing coded text do not result in any significant reduction in storage space requirements. The simple expedient of sampling at lower sampling rates is not practical in the storage of digitized data for correlation because high sampling rates are needed in order to minimize phase error which otherwise adversely affects correlation scores.
It is known in the art to sample at high rates and convert the digital data sampling rates to a lower rate, for example, to transmit speech at a very low bit rate. It is also known to convert digital data sampled at one rate to a higher sampling rate in order to extract a narrow band of the spectrum of a signal for high resolution spectrum analysis. This is known as frequency zoom and is used in sonar and vibration signal analysis. Such methods are taught in Section 2.8 of chapter 2 in Digital Signal Processing Theory, Design, and Implementation by Abraham Peled and Bede Liu, published in 1976 by John Wiley and Sons, of New York.